Overview

Dataset statistics

Number of variables20
Number of observations5630
Missing cells1856
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory879.8 KiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical9

Warnings

Tenure has 264 (4.7%) missing values Missing
WarehouseToHome has 251 (4.5%) missing values Missing
HourSpendOnApp has 255 (4.5%) missing values Missing
OrderAmountHikeFromlastYear has 265 (4.7%) missing values Missing
CouponUsed has 256 (4.5%) missing values Missing
OrderCount has 258 (4.6%) missing values Missing
DaySinceLastOrder has 307 (5.5%) missing values Missing
CustomerID is uniformly distributed Uniform
CustomerID has unique values Unique
Tenure has 508 (9.0%) zeros Zeros
CouponUsed has 1030 (18.3%) zeros Zeros
DaySinceLastOrder has 496 (8.8%) zeros Zeros

Reproduction

Analysis started2021-02-15 19:46:33.592551
Analysis finished2021-02-15 19:47:00.329496
Duration26.74 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

CustomerID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct5630
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52815.5
Minimum50001
Maximum55630
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:00.485196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum50001
5-th percentile50282.45
Q151408.25
median52815.5
Q354222.75
95-th percentile55348.55
Maximum55630
Range5629
Interquartile range (IQR)2814.5

Descriptive statistics

Standard deviation1625.385339
Coefficient of variation (CV)0.03077477897
Kurtosis-1.2
Mean52815.5
Median Absolute Deviation (MAD)1407.5
Skewness0
Sum297351265
Variance2641877.5
MonotocityStrictly increasing
2021-02-15T14:47:00.645833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511991
 
< 0.1%
540271
 
< 0.1%
540111
 
< 0.1%
519641
 
< 0.1%
540151
 
< 0.1%
519681
 
< 0.1%
540191
 
< 0.1%
519721
 
< 0.1%
540231
 
< 0.1%
519761
 
< 0.1%
Other values (5620)5620
99.8%
ValueCountFrequency (%)
500011
< 0.1%
500021
< 0.1%
500031
< 0.1%
500041
< 0.1%
500051
< 0.1%
ValueCountFrequency (%)
556301
< 0.1%
556291
< 0.1%
556281
< 0.1%
556271
< 0.1%
556261
< 0.1%

Churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4682 
1
948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%
2021-02-15T14:47:00.928276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:01.008243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Most occurring characters

ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5630
100.0%

Most frequent character per category

ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Most occurring scripts

ValueCountFrequency (%)
Common5630
100.0%

Most frequent character per script

ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5630
100.0%

Most frequent character per block

ValueCountFrequency (%)
04682
83.2%
1948
 
16.8%

Tenure
Real number (ℝ≥0)

MISSING
ZEROS

Distinct36
Distinct (%)0.7%
Missing264
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean10.18989937
Minimum0
Maximum61
Zeros508
Zeros (%)9.0%
Memory size44.1 KiB
2021-02-15T14:47:01.102860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q316
95-th percentile27
Maximum61
Range61
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.557240984
Coefficient of variation (CV)0.8397767904
Kurtosis-0.007369469517
Mean10.18989937
Median Absolute Deviation (MAD)7
Skewness0.7365133839
Sum54679
Variance73.22637326
MonotocityNot monotonic
2021-02-15T14:47:01.256843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1690
 
12.3%
0508
 
9.0%
8263
 
4.7%
9247
 
4.4%
7221
 
3.9%
10213
 
3.8%
5204
 
3.6%
4203
 
3.6%
3195
 
3.5%
11194
 
3.4%
Other values (26)2428
43.1%
(Missing)264
 
4.7%
ValueCountFrequency (%)
0508
9.0%
1690
12.3%
2167
 
3.0%
3195
 
3.5%
4203
 
3.6%
ValueCountFrequency (%)
611
 
< 0.1%
601
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
3149
0.9%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Mobile Phone
2765 
Computer
1634 
Phone
1231 

Length

Max length12
Median length8
Mean length9.308525755
Min length5

Characters and Unicode

Total characters52407
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile Phone
2nd rowPhone
3rd rowPhone
4th rowPhone
5th rowPhone
ValueCountFrequency (%)
Mobile Phone2765
49.1%
Computer1634
29.0%
Phone1231
21.9%
2021-02-15T14:47:01.556830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:01.647588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
phone3996
47.6%
mobile2765
32.9%
computer1634
19.5%

Most occurring characters

ValueCountFrequency (%)
o8395
16.0%
e8395
16.0%
P3996
7.6%
h3996
7.6%
n3996
7.6%
M2765
 
5.3%
b2765
 
5.3%
i2765
 
5.3%
l2765
 
5.3%
2765
 
5.3%
Other values (6)9804
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41247
78.7%
Uppercase Letter8395
 
16.0%
Space Separator2765
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
o8395
20.4%
e8395
20.4%
h3996
9.7%
n3996
9.7%
b2765
 
6.7%
i2765
 
6.7%
l2765
 
6.7%
m1634
 
4.0%
p1634
 
4.0%
u1634
 
4.0%
Other values (2)3268
 
7.9%
ValueCountFrequency (%)
P3996
47.6%
M2765
32.9%
C1634
19.5%
ValueCountFrequency (%)
2765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49642
94.7%
Common2765
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
o8395
16.9%
e8395
16.9%
P3996
8.0%
h3996
8.0%
n3996
8.0%
M2765
 
5.6%
b2765
 
5.6%
i2765
 
5.6%
l2765
 
5.6%
C1634
 
3.3%
Other values (5)8170
16.5%
ValueCountFrequency (%)
2765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII52407
100.0%

Most frequent character per block

ValueCountFrequency (%)
o8395
16.0%
e8395
16.0%
P3996
7.6%
h3996
7.6%
n3996
7.6%
M2765
 
5.3%
b2765
 
5.3%
i2765
 
5.3%
l2765
 
5.3%
2765
 
5.3%
Other values (6)9804
18.7%

CityTier
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
1
3666 
3
1722 
2
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row1
ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%
2021-02-15T14:47:01.910359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:01.988854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Most occurring characters

ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5630
100.0%

Most frequent character per category

ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common5630
100.0%

Most frequent character per script

ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5630
100.0%

Most frequent character per block

ValueCountFrequency (%)
13666
65.1%
31722
30.6%
2242
 
4.3%

WarehouseToHome
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)0.6%
Missing251
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean15.63989589
Minimum5
Maximum127
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:02.085080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q320
95-th percentile33
Maximum127
Range122
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.531475187
Coefficient of variation (CV)0.545494372
Kurtosis9.986930421
Mean15.63989589
Median Absolute Deviation (MAD)5
Skewness1.619153668
Sum84127
Variance72.78606886
MonotocityNot monotonic
2021-02-15T14:47:02.237566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
9559
 
9.9%
8444
 
7.9%
7389
 
6.9%
16322
 
5.7%
14299
 
5.3%
6295
 
5.2%
15288
 
5.1%
10274
 
4.9%
13249
 
4.4%
11233
 
4.1%
Other values (24)2027
36.0%
(Missing)251
 
4.5%
ValueCountFrequency (%)
58
 
0.1%
6295
5.2%
7389
6.9%
8444
7.9%
9559
9.9%
ValueCountFrequency (%)
1271
 
< 0.1%
1261
 
< 0.1%
3651
0.9%
3593
1.7%
3463
1.1%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Debit Card
2314 
Credit Card
1501 
E wallet
614 
UPI
414 
COD
365 
Other values (2)
422 

Length

Max length16
Median length10
Mean length8.85079929
Min length2

Characters and Unicode

Total characters49830
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebit Card
2nd rowUPI
3rd rowDebit Card
4th rowDebit Card
5th rowCC
ValueCountFrequency (%)
Debit Card2314
41.1%
Credit Card1501
26.7%
E wallet614
 
10.9%
UPI414
 
7.4%
COD365
 
6.5%
CC273
 
4.8%
Cash on Delivery149
 
2.6%
2021-02-15T14:47:02.521668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:02.623858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
card3815
36.8%
debit2314
22.3%
credit1501
 
14.5%
e614
 
5.9%
wallet614
 
5.9%
upi414
 
4.0%
cod365
 
3.5%
cc273
 
2.6%
delivery149
 
1.4%
cash149
 
1.4%

Most occurring characters

ValueCountFrequency (%)
C6376
12.8%
r5465
11.0%
d5316
10.7%
e4727
9.5%
4727
9.5%
a4578
9.2%
t4429
8.9%
i3964
8.0%
D2828
5.7%
b2314
 
4.6%
Other values (13)5106
10.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter33678
67.6%
Uppercase Letter11425
 
22.9%
Space Separator4727
 
9.5%

Most frequent character per category

ValueCountFrequency (%)
r5465
16.2%
d5316
15.8%
e4727
14.0%
a4578
13.6%
t4429
13.2%
i3964
11.8%
b2314
6.9%
l1377
 
4.1%
w614
 
1.8%
s149
 
0.4%
Other values (5)745
 
2.2%
ValueCountFrequency (%)
C6376
55.8%
D2828
24.8%
E614
 
5.4%
U414
 
3.6%
P414
 
3.6%
I414
 
3.6%
O365
 
3.2%
ValueCountFrequency (%)
4727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45103
90.5%
Common4727
 
9.5%

Most frequent character per script

ValueCountFrequency (%)
C6376
14.1%
r5465
12.1%
d5316
11.8%
e4727
10.5%
a4578
10.2%
t4429
9.8%
i3964
8.8%
D2828
6.3%
b2314
 
5.1%
l1377
 
3.1%
Other values (12)3729
8.3%
ValueCountFrequency (%)
4727
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII49830
100.0%

Most frequent character per block

ValueCountFrequency (%)
C6376
12.8%
r5465
11.0%
d5316
10.7%
e4727
9.5%
4727
9.5%
a4578
9.2%
t4429
8.9%
i3964
8.0%
D2828
5.7%
b2314
 
4.6%
Other values (13)5106
10.2%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Male
3384 
Female
2246 

Length

Max length6
Median length4
Mean length4.797868561
Min length4

Characters and Unicode

Total characters27012
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale
ValueCountFrequency (%)
Male3384
60.1%
Female2246
39.9%
2021-02-15T14:47:02.886715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:02.979202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
male3384
60.1%
female2246
39.9%

Most occurring characters

ValueCountFrequency (%)
e7876
29.2%
a5630
20.8%
l5630
20.8%
M3384
12.5%
F2246
 
8.3%
m2246
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21382
79.2%
Uppercase Letter5630
 
20.8%

Most frequent character per category

ValueCountFrequency (%)
e7876
36.8%
a5630
26.3%
l5630
26.3%
m2246
 
10.5%
ValueCountFrequency (%)
M3384
60.1%
F2246
39.9%

Most occurring scripts

ValueCountFrequency (%)
Latin27012
100.0%

Most frequent character per script

ValueCountFrequency (%)
e7876
29.2%
a5630
20.8%
l5630
20.8%
M3384
12.5%
F2246
 
8.3%
m2246
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII27012
100.0%

Most frequent character per block

ValueCountFrequency (%)
e7876
29.2%
a5630
20.8%
l5630
20.8%
M3384
12.5%
F2246
 
8.3%
m2246
 
8.3%

HourSpendOnApp
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)0.1%
Missing255
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2.931534884
Minimum0
Maximum5
Zeros3
Zeros (%)0.1%
Memory size44.1 KiB
2021-02-15T14:47:03.048972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72192585
Coefficient of variation (CV)0.2462620704
Kurtosis-0.667076137
Mean2.931534884
Median Absolute Deviation (MAD)1
Skewness-0.02721262163
Sum15757
Variance0.5211769329
MonotocityNot monotonic
2021-02-15T14:47:03.160201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
32687
47.7%
21471
26.1%
41176
20.9%
135
 
0.6%
53
 
0.1%
03
 
0.1%
(Missing)255
 
4.5%
ValueCountFrequency (%)
03
 
0.1%
135
 
0.6%
21471
26.1%
32687
47.7%
41176
20.9%
ValueCountFrequency (%)
53
 
0.1%
41176
20.9%
32687
47.7%
21471
26.1%
135
 
0.6%

NumberOfDeviceRegistered
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.688987567
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:03.262232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.023998519
Coefficient of variation (CV)0.2775825346
Kurtosis0.5828487316
Mean3.688987567
Median Absolute Deviation (MAD)1
Skewness-0.3969686435
Sum20769
Variance1.048572967
MonotocityNot monotonic
2021-02-15T14:47:03.371652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
42377
42.2%
31699
30.2%
5881
 
15.6%
2276
 
4.9%
1235
 
4.2%
6162
 
2.9%
ValueCountFrequency (%)
1235
 
4.2%
2276
 
4.9%
31699
30.2%
42377
42.2%
5881
 
15.6%
ValueCountFrequency (%)
6162
 
2.9%
5881
 
15.6%
42377
42.2%
31699
30.2%
2276
 
4.9%

PreferedOrderCat
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Laptop & Accessory
2050 
Mobile Phone
1271 
Fashion
826 
Mobile
809 
Grocery
410 

Length

Max length18
Median length12
Mean length11.94351687
Min length6

Characters and Unicode

Total characters67242
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLaptop & Accessory
2nd rowMobile
3rd rowMobile
4th rowLaptop & Accessory
5th rowMobile
ValueCountFrequency (%)
Laptop & Accessory2050
36.4%
Mobile Phone1271
22.6%
Fashion826
14.7%
Mobile809
 
14.4%
Grocery410
 
7.3%
Others264
 
4.7%
2021-02-15T14:47:03.638686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:03.727553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
mobile2080
18.9%
accessory2050
18.6%
laptop2050
18.6%
2050
18.6%
phone1271
11.6%
fashion826
 
7.5%
grocery410
 
3.7%
others264
 
2.4%

Most occurring characters

ValueCountFrequency (%)
o8687
 
12.9%
e6075
 
9.0%
5371
 
8.0%
s5190
 
7.7%
c4510
 
6.7%
p4100
 
6.1%
r3134
 
4.7%
i2906
 
4.3%
a2876
 
4.3%
y2460
 
3.7%
Other values (13)21933
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50870
75.7%
Uppercase Letter8951
 
13.3%
Space Separator5371
 
8.0%
Other Punctuation2050
 
3.0%

Most frequent character per category

ValueCountFrequency (%)
o8687
17.1%
e6075
11.9%
s5190
10.2%
c4510
8.9%
p4100
8.1%
r3134
 
6.2%
i2906
 
5.7%
a2876
 
5.7%
y2460
 
4.8%
h2361
 
4.6%
Other values (4)8571
16.8%
ValueCountFrequency (%)
M2080
23.2%
L2050
22.9%
A2050
22.9%
P1271
14.2%
F826
 
9.2%
G410
 
4.6%
O264
 
2.9%
ValueCountFrequency (%)
5371
100.0%
ValueCountFrequency (%)
&2050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59821
89.0%
Common7421
 
11.0%

Most frequent character per script

ValueCountFrequency (%)
o8687
14.5%
e6075
 
10.2%
s5190
 
8.7%
c4510
 
7.5%
p4100
 
6.9%
r3134
 
5.2%
i2906
 
4.9%
a2876
 
4.8%
y2460
 
4.1%
h2361
 
3.9%
Other values (11)17522
29.3%
ValueCountFrequency (%)
5371
72.4%
&2050
 
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII67242
100.0%

Most frequent character per block

ValueCountFrequency (%)
o8687
 
12.9%
e6075
 
9.0%
5371
 
8.0%
s5190
 
7.7%
c4510
 
6.7%
p4100
 
6.1%
r3134
 
4.7%
i2906
 
4.3%
a2876
 
4.3%
y2460
 
3.7%
Other values (13)21933
32.6%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
3
1698 
1
1164 
5
1108 
4
1074 
2
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5
ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%
2021-02-15T14:47:03.970838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:04.051239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Most occurring characters

ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5630
100.0%

Most frequent character per category

ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common5630
100.0%

Most frequent character per script

ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5630
100.0%

Most frequent character per block

ValueCountFrequency (%)
31698
30.2%
11164
20.7%
51108
19.7%
41074
19.1%
2586
 
10.4%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Married
2986 
Single
1796 
Divorced
848 

Length

Max length8
Median length7
Mean length6.831616341
Min length6

Characters and Unicode

Total characters38462
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowSingle
ValueCountFrequency (%)
Married2986
53.0%
Single1796
31.9%
Divorced848
 
15.1%
2021-02-15T14:47:04.285268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:04.382454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
married2986
53.0%
single1796
31.9%
divorced848
 
15.1%

Most occurring characters

ValueCountFrequency (%)
r6820
17.7%
i5630
14.6%
e5630
14.6%
d3834
10.0%
M2986
7.8%
a2986
7.8%
S1796
 
4.7%
n1796
 
4.7%
g1796
 
4.7%
l1796
 
4.7%
Other values (4)3392
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32832
85.4%
Uppercase Letter5630
 
14.6%

Most frequent character per category

ValueCountFrequency (%)
r6820
20.8%
i5630
17.1%
e5630
17.1%
d3834
11.7%
a2986
9.1%
n1796
 
5.5%
g1796
 
5.5%
l1796
 
5.5%
v848
 
2.6%
o848
 
2.6%
ValueCountFrequency (%)
M2986
53.0%
S1796
31.9%
D848
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Latin38462
100.0%

Most frequent character per script

ValueCountFrequency (%)
r6820
17.7%
i5630
14.6%
e5630
14.6%
d3834
10.0%
M2986
7.8%
a2986
7.8%
S1796
 
4.7%
n1796
 
4.7%
g1796
 
4.7%
l1796
 
4.7%
Other values (4)3392
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII38462
100.0%

Most frequent character per block

ValueCountFrequency (%)
r6820
17.7%
i5630
14.6%
e5630
14.6%
d3834
10.0%
M2986
7.8%
a2986
7.8%
S1796
 
4.7%
n1796
 
4.7%
g1796
 
4.7%
l1796
 
4.7%
Other values (4)3392
8.8%

NumberOfAddress
Real number (ℝ≥0)

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.214031972
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:04.474247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile10
Maximum22
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.583585513
Coefficient of variation (CV)0.6130911037
Kurtosis0.9592292732
Mean4.214031972
Median Absolute Deviation (MAD)1
Skewness1.088639383
Sum23725
Variance6.674914101
MonotocityNot monotonic
2021-02-15T14:47:04.593920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
21369
24.3%
31278
22.7%
4588
10.4%
5571
10.1%
6382
 
6.8%
1371
 
6.6%
8280
 
5.0%
7256
 
4.5%
9239
 
4.2%
10194
 
3.4%
Other values (5)102
 
1.8%
ValueCountFrequency (%)
1371
 
6.6%
21369
24.3%
31278
22.7%
4588
10.4%
5571
10.1%
ValueCountFrequency (%)
221
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
1198
1.7%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
0
4026 
1
1604 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5630
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0
ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%
2021-02-15T14:47:04.834402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-15T14:47:04.910582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Most occurring characters

ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5630
100.0%

Most frequent character per category

ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Most occurring scripts

ValueCountFrequency (%)
Common5630
100.0%

Most frequent character per script

ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII5630
100.0%

Most frequent character per block

ValueCountFrequency (%)
04026
71.5%
11604
 
28.5%

OrderAmountHikeFromlastYear
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.3%
Missing265
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean15.70792171
Minimum11
Maximum26
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:04.984821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median15
Q318
95-th percentile23
Maximum26
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.675485463
Coefficient of variation (CV)0.2339892908
Kurtosis-0.2803811889
Mean15.70792171
Median Absolute Deviation (MAD)3
Skewness0.7907853591
Sum84273
Variance13.50919339
MonotocityNot monotonic
2021-02-15T14:47:05.370845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14750
13.3%
13741
13.2%
12728
12.9%
15542
9.6%
11391
6.9%
16333
5.9%
18321
5.7%
19311
5.5%
17297
 
5.3%
20243
 
4.3%
Other values (6)708
12.6%
(Missing)265
 
4.7%
ValueCountFrequency (%)
11391
6.9%
12728
12.9%
13741
13.2%
14750
13.3%
15542
9.6%
ValueCountFrequency (%)
2633
 
0.6%
2573
 
1.3%
2484
1.5%
23144
2.6%
22184
3.3%

CouponUsed
Real number (ℝ≥0)

MISSING
ZEROS

Distinct17
Distinct (%)0.3%
Missing256
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.751023446
Minimum0
Maximum16
Zeros1030
Zeros (%)18.3%
Memory size44.1 KiB
2021-02-15T14:47:05.501209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile6
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.894621447
Coefficient of variation (CV)1.08200804
Kurtosis9.132281171
Mean1.751023446
Median Absolute Deviation (MAD)1
Skewness2.545652562
Sum9410
Variance3.589590428
MonotocityNot monotonic
2021-02-15T14:47:05.627448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12105
37.4%
21283
22.8%
01030
18.3%
3327
 
5.8%
4197
 
3.5%
5129
 
2.3%
6108
 
1.9%
789
 
1.6%
842
 
0.7%
1014
 
0.2%
Other values (7)50
 
0.9%
(Missing)256
 
4.5%
ValueCountFrequency (%)
01030
18.3%
12105
37.4%
21283
22.8%
3327
 
5.8%
4197
 
3.5%
ValueCountFrequency (%)
162
 
< 0.1%
151
 
< 0.1%
145
0.1%
138
0.1%
129
0.2%

OrderCount
Real number (ℝ≥0)

MISSING

Distinct16
Distinct (%)0.3%
Missing258
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean3.008004468
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size44.1 KiB
2021-02-15T14:47:05.756001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.939679548
Coefficient of variation (CV)0.9772856323
Kurtosis4.718466052
Mean3.008004468
Median Absolute Deviation (MAD)1
Skewness2.196414108
Sum16159
Variance8.641715846
MonotocityNot monotonic
2021-02-15T14:47:05.875275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
22025
36.0%
11751
31.1%
3371
 
6.6%
7206
 
3.7%
4204
 
3.6%
5181
 
3.2%
8172
 
3.1%
6137
 
2.4%
962
 
1.1%
1254
 
1.0%
Other values (6)209
 
3.7%
(Missing)258
 
4.6%
ValueCountFrequency (%)
11751
31.1%
22025
36.0%
3371
 
6.6%
4204
 
3.6%
5181
 
3.2%
ValueCountFrequency (%)
1623
0.4%
1533
0.6%
1436
0.6%
1330
0.5%
1254
1.0%

DaySinceLastOrder
Real number (ℝ≥0)

MISSING
ZEROS

Distinct22
Distinct (%)0.4%
Missing307
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean4.543490513
Minimum0
Maximum46
Zeros496
Zeros (%)8.8%
Memory size44.1 KiB
2021-02-15T14:47:06.003598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum46
Range46
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.654433197
Coefficient of variation (CV)0.8043228409
Kurtosis4.023964341
Mean4.543490513
Median Absolute Deviation (MAD)2
Skewness1.190999503
Sum24185
Variance13.35488199
MonotocityNot monotonic
2021-02-15T14:47:06.131402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3900
16.0%
2792
14.1%
1614
10.9%
8538
9.6%
0496
8.8%
7447
7.9%
4431
7.7%
9299
 
5.3%
5228
 
4.0%
10157
 
2.8%
Other values (12)421
7.5%
(Missing)307
 
5.5%
ValueCountFrequency (%)
0496
8.8%
1614
10.9%
2792
14.1%
3900
16.0%
4431
7.7%
ValueCountFrequency (%)
461
 
< 0.1%
311
 
< 0.1%
301
 
< 0.1%
1810
0.2%
1717
0.3%

CashbackAmount
Real number (ℝ≥0)

Distinct220
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.221492
Minimum0
Maximum325
Zeros4
Zeros (%)0.1%
Memory size44.1 KiB
2021-02-15T14:47:06.275762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile123
Q1146
median163
Q3196
95-th percentile292
Maximum325
Range325
Interquartile range (IQR)50

Descriptive statistics

Standard deviation49.19386891
Coefficient of variation (CV)0.2775841031
Kurtosis0.9735461687
Mean177.221492
Median Absolute Deviation (MAD)23
Skewness1.149594996
Sum997757
Variance2420.036738
MonotocityNot monotonic
2021-02-15T14:47:06.433483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148134
 
2.4%
149120
 
2.1%
146118
 
2.1%
152116
 
2.1%
153108
 
1.9%
12397
 
1.7%
15195
 
1.7%
15489
 
1.6%
14787
 
1.5%
15084
 
1.5%
Other values (210)4582
81.4%
ValueCountFrequency (%)
04
0.1%
121
 
< 0.1%
254
0.1%
371
 
< 0.1%
561
 
< 0.1%
ValueCountFrequency (%)
3254
 
0.1%
3246
0.1%
3236
0.1%
32210
0.2%
32112
0.2%

Interactions

2021-02-15T14:46:43.515024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:43.686076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:43.817019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:43.940389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.069878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.190413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.326448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.452774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.575892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.693356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.817315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:44.937732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.064858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.190485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.313205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.434009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.562095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.691588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.820786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:45.940771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.068342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.196537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.329468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.453982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.579529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.727979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:46.882781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:47.190572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:47.374854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:47.666538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:47.834084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:47.959257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.100507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.223586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.343442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.478101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.641042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.793700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:48.921425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.036584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.160884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.282968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.405517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.527922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.649886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.769077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:49.902856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.030715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.167055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.350835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.493425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.614080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.728984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.846600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:50.971450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.102623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.228568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.349597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.468915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.579850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.695641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.824724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:51.956465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.085519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.210765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.499053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.631294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.764471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:52.914102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.043773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.184376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.318827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.453534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.587314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.728433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.861989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:53.992582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.132332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.268772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.399314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.534924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.659978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.786781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:54.913543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.035809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.163793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.286909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.419953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.552754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.682530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.815380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:55.938807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.060941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.181601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.340973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.513979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.640578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.777365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:56.925629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.071874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.212509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.351335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.476132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.603263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.722169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.848111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:57.970342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:58.101808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:58.243129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-15T14:46:58.377345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-15T14:47:06.592996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-15T14:47:06.837101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-15T14:47:07.073414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-15T14:47:07.320238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-15T14:47:07.574212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-15T14:46:58.893288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-15T14:46:59.463444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-15T14:46:59.863393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-15T14:47:00.095371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
05000114.0Mobile Phone36.0Debit CardFemale3.03Laptop & Accessory2Single9111.01.01.05.0160
1500021NaNPhone18.0UPIMale3.04Mobile3Single7115.00.01.00.0121
2500031NaNPhone130.0Debit CardMale2.04Mobile3Single6114.00.01.03.0120
35000410.0Phone315.0Debit CardMale2.04Laptop & Accessory5Single8023.00.01.03.0134
45000510.0Phone112.0CCMaleNaN3Mobile5Single3011.01.01.03.0130
55000610.0Computer122.0Debit CardFemale3.05Mobile Phone5Single2122.04.06.07.0139
6500071NaNPhone311.0Cash on DeliveryMale2.03Laptop & Accessory2Divorced4014.00.01.00.0121
7500081NaNPhone16.0CCMale3.03Mobile2Divorced3116.02.02.00.0123
850009113.0Phone39.0E walletMaleNaN4Mobile3Divorced2114.00.01.02.0127
9500101NaNPhone131.0Debit CardMale2.05Mobile3Single2012.01.01.01.0123

Last rows

CustomerIDChurnTenurePreferredLoginDeviceCityTierWarehouseToHomePreferredPaymentModeGenderHourSpendOnAppNumberOfDeviceRegisteredPreferedOrderCatSatisfactionScoreMaritalStatusNumberOfAddressComplainOrderAmountHikeFromlastYearCouponUsedOrderCountDaySinceLastOrderCashbackAmount
56205562103.0Mobile Phone135.0Credit CardFemale4.05Mobile Phone5Single3015.01.02.05.0163
562155622114.0Mobile Phone335.0E walletMale3.05Fashion5Married6114.03.0NaN1.0234
562255623013.0Mobile Phone331.0E walletFemale3.05Grocery1Married2012.04.0NaN7.0245
56235562405.0Computer112.0Credit CardMale4.04Laptop & Accessory5Single2020.02.02.0NaN224
56245562501.0Mobile Phone312.0UPIFemale2.05Mobile Phone3Single2019.02.02.01.0155
562555626010.0Computer130.0Credit CardMale3.02Laptop & Accessory1Married6018.01.02.04.0151
562655627013.0Mobile Phone113.0Credit CardMale3.05Fashion5Married6016.01.02.0NaN225
56275562801.0Mobile Phone111.0Debit CardMale3.02Laptop & Accessory4Married3121.01.02.04.0186
562855629023.0Computer39.0Credit CardMale4.05Laptop & Accessory4Married4015.02.02.09.0179
56295563008.0Mobile Phone115.0Credit CardMale3.02Laptop & Accessory3Married4013.02.02.03.0169